Adjusting parameters used in pulse oximetry analysis

ABSTRACT

Adjusting a pulse qualification criterion includes receiving a signal representing a plurality of pulses, where the signal is generated in response to detecting light scattered from blood perfused tissue. A characteristic is determined. A pulse qualification criterion used for qualifying a pulse is adjusted in accordance with the characteristic. The pulses are evaluated according to the pulse qualification criterion.

RELATED APPLICATIONS

This application claims the benefit under 35 U.S.C. § 121 of U.S.application Ser. No. 11/261,012 filed on Oct. 28, 2005, which isincorporated herein by reference in its entirety.

TECHNICAL FIELD

This invention relates generally to the field of medical devices and,more particularly, to adjusting parameters used in pulse oximetryanalysis.

BACKGROUND

This section is intended to introduce the reader to various aspects ofart that may be related to various aspects of the present invention,which are described and/or claimed below. This discussion is believed tobe helpful in providing the reader with background information tofacilitate a better understanding of the various aspects of the presentinvention. Accordingly, it should be understood that these statementsare to be read in this light, and not as admissions of prior art.

A pulse oximeter is a medical device that may be used to measure variousblood characteristics, for example, the oxygen saturation of hemoglobinin pulsing blood and/or the pulse rate of a patient. To measure thesecharacteristics, a non-invasive sensor may be used to pass light througha portion of blood perfused tissue and photo-electrically sense theabsorption and scattering of light in the tissue. The amount of lightabsorbed and/or scattered is analyzed to estimate the amount of bloodconstituent in the tissue.

A detector signal resulting from measurement of the light describes theblood characteristics. As an example, pulses refer to the varying amountof arterial blood present in the tissue during a cardiac cycle. Thevarying amount of arterial blood yields cyclic attenuation of the lightpassing through the tissue. Accordingly, the detector signal frommeasurement of the light exhibits the familiar plethysmographicwaveform.

Analysis of detector signals involves processes that use variousparameters. As an example, the analysis may involve filtering estimatesof hemoglobin saturation to improve the accuracy of the saturationestimates. As another example, the analysis may involve filtering ofplethysmographic waveforms. The filtering may use parameters such asfilter weights or coefficients to adjust the filtering process. Asanother example, the analysis may involve applying pulse qualificationcriteria to qualify or disqualify pulses. The pulse qualificationcriteria may include parameters used to adjust the pulse qualification.

It is desirable to provide a flexible and robust methodology foradjusting the parameters of oximetry analysis.

SUMMARY

Certain aspects commensurate in scope with the originally claimedinvention are set forth below. It should be understood that theseaspects are presented merely to provide the reader with a brief summaryof certain forms the invention might take and that these aspects are notintended to limit the scope of the invention. Indeed, the invention mayencompass a variety of aspects that may not be set forth below.

In accordance with one aspect of the present invention, there isprovided a method for adjusting a pulse qualification criterion. Themethod may include receiving a signal representing a plurality ofpulses, where the signal is generated in response to detecting lightscattered from blood perfused tissue. A filter parameter value of afilter parameter of a filter may be determined, where the filter may beoperable to filter the signal. A pulse qualification criterion may beadjusted in accordance with the filter parameter value, where the pulsequalification criterion may be used for qualifying a pulse. The pulsesmay be evaluated according to the pulse qualification criterion.

In accordance with another aspect of the present invention, there isprovided a method for adjusting a pulse qualification criterion. Themethod may include receiving a signal representing a plurality ofpulses, where the signal may be generated in response to detecting lightscattered from blood perfused tissue. Each pulse may have an amplitudeand a period, and a subset of the pulses may have a plurality ofamplitudes and a plurality of periods. An average amplitude may bedetermined from the plurality of amplitudes, and an average period maybe determined from the plurality of periods. A pulse qualificationcriterion may be adjusted in accordance with the average amplitude andthe average period. Pulses may be evaluated according to the pulsequalification criterion.

In accordance with another aspect of the present invention, there isprovided a method for adjusting a filter weight of a saturationfiltering process. The method may include receiving a signalrepresenting a plurality of pulses, where the signal may be generated inresponse to detecting light scattered from blood perfused tissue. Thelight may comprise a red waveform and an infrared waveform. Aratio-of-ratios variability metric indicating the variation of aratio-of-ratios may be determined. A ratio-of-ratios may represent theratio of absorbances of the red waveform and the infrared waveform. Apulse quality metric indicating the quality of one or more pulses may bedetermined. A saturation filter weight may be adjusted in accordancewith the ratio-of-ratios variability metric and the pulse qualitymetric, where the saturation filter weight may represent a weight usedfor a filtering process operable to filter a saturation estimate of theblood perfused tissue.

BRIEF DESCRIPTION OF THE DRAWINGS

Certain exemplary embodiments are described in the following detaileddescription and in reference to the drawings in which:

FIG. 1 is a block diagram of one embodiment of a pulse oximeter that maybe configured to implement embodiments of the present invention;

FIG. 2 is a block diagram of a signal processing system of a pulseoximeter in accordance with one embodiment of the present invention;

FIG. 3 is a flowchart illustrating one embodiment of a method foradjusting a noise gate parameter of a noise gate criterion in accordancewith an ensemble averaging weight;

FIG. 4 is a flowchart illustrating one embodiment of a method foradjusting a pulse period criterion in accordance with an ensembleaveraging weight;

FIG. 5 is a flowchart illustrating one embodiment of a method foradjusting a pulse amplitude criterion in accordance with an averagepulse amplitude and an average pulse period;

FIG. 6 is a flowchart illustrating one embodiment of a method foradjusting a saturation weight in accordance with a pulse quality metric;and

FIG. 7 is a flowchart illustrating an embodiment of a method foradjusting a saturation weight in accordance with a ratio-of-ratiosvariability metric and a pulse quality metric.

DETAILED DESCRIPTION

The exemplary embodiments described below are best understood byreferring to FIGS. 1 through 6 of the drawings, like numerals being usedfor like and corresponding parts of the various drawings. The methodsand systems in accordance with these exemplary embodiments are directedtowards adjusting parameters of oximetry analysis. These embodiments maybe particularly applicable to and thus, are explained by reference tomeasuring oxygen saturation and qualifying pulses, as applicable topulse oximeter monitors and pulse oximetry sensors. It should berealized, however, that the embodiments may be applicable to anygeneralized patient monitor and associated patient sensor, such as, forexample, an electrocardiograph (ECG), blood pressure monitor, etc., andare thus, also applicable to nonoximetry methods and systems.

FIG. 1 is a block diagram of one embodiment of a pulse oximeter that maybe configured to implement certain techniques, as described in detailbelow. The techniques may be implemented as a data processing procedurethat is executed by a oximeter 120 having a microprocessor 122, asdescribed below. A sensor 100 illuminates blood perfused tissue 112,detects light scattered by tissue 112, and generates detector signals.According to the illustrated embodiment, sensor 100 may comprise a lightsource 110, a photodetector 114, and an encoder 116. Light from lightsource 110 passes into blood perfused tissue 112. Photodetector 114detects the scattered light and generates detector signals representingthe detected light. Encoder 116 provides signals indicative of thewavelength of light source 110 to allow the oximeter 120 to selectappropriate calibration coefficients for calculating oxygen saturation.

Pulse oximeter 120 analyzes the detector signals. According to theillustrated embodiment, pulse oximeter 120 includes general processingand interface components such as a microprocessor 122, a ROM 146, a RAM126, a display 128, and control inputs 154 coupled to an internal bus124. Pulse oximeter 120 also includes components that operate to controlthe light that passes through tissue 112. According to the illustratedembodiment, pulse oximeter 120 includes a time processing unit (TPU) 130and light drive circuitry 132. TPU 130 provides timing control signalsto light drive circuitry 132. Light drive circuitry 132 controls whenlight source 110 is illuminated, and may control multiplexed timing ifmultiple light sources 110 are used.

Signals from detector 114 are received through amplifier 133. Pulseoximeter 120 includes components that operate to process the receivedsignal. According to the illustrated embodiment, pulse oximeter 120includes a switching circuit 134, an amplifier 136, a low pass filter138, an analog-to-digital converter 140, and a queued serial module(QSM) 142. Switching circuit 134 controls the sampling of the signals inresponse to instructions from TPU 130. If multiple light sources areused, the sampling times may depend upon which of the light sources 110are illuminated.

The signals from the switch 134 are passed through amplifier 136, lowpass filter 138, and analog-to-digital converter 140. Digital data fromthe signals is then stored in a queued serial module (QSM) 142. Thedigital data may be downloaded to RAM 126 as QSM 142 is filled. In oneembodiment, there may be multiple parallel paths of separate amplifier,filter, and analog-to-digital converters for different lightwavelengths.

Microprocessor 122 calculates oxygen saturation based on the values ofthe received signals. ROM 146 may store coefficients used in thecalculations. Detector/decoder 144 selects the appropriate coefficientsaccording to signals received from encoder 116. Control inputs 154receive input data and instructions, and may comprise, for instance, aswitch on the pulse oximeter, a keyboard, or a port providinginstructions from a remote host computer. Display 128 provides feedbackand results of the analysis.

One or more components of pulse oximeter 120 may include appropriateinput devices, output devices, mass storage media, processors, memory,or other components for receiving, processing, storing, andcommunicating information according to the operation of pulse oximeter120. As an example, one or more components of pulse oximeter 120 mayinclude logic, an interface, memory, or any suitable combination of thepreceding. By way of example, “logic” may refer to hardware, software,firmware, or any suitable combination of the preceding. Certain logicmay manage the operation of a device, and may comprise, for example, aprocessor. “Processor” may refer to any suitable device operable toexecute instructions and manipulate data to perform operations.

“Interface” may refer to logic of a device operable to receive input forthe device, send output from the device, perform suitable processing ofthe input or output or both, or any combination of the preceding, andmay comprise one or more ports, conversion software, or both. “Memory”may refer to logic operable to store and facilitate retrieval ofinformation, and may comprise Random Access Memory (RAM), Read OnlyMemory (ROM), a magnetic drive, a disk drive, a Compact Disk (CD) drive,a Digital Video Disk (DVD) drive, removable media storage, any othersuitable data storage medium, or a combination of any of the preceding.

Modifications, additions, or omissions may be made to pulse oximeter 120without departing from the scope of the invention. The components ofpulse oximeter 120 may be integrated or separated according toparticular needs. Moreover, the operations of pulse oximeter 120 may beperformed by more, fewer, or other modules. Additionally, operations ofpulse oximeter 120 may be performed using any suitable logic comprisingsoftware, hardware, other logic, or any suitable combination of thepreceding.

The brief description of an exemplary pulse oximeter set forth above,serves as a basis for describing the exemplary methods for adjustingoximetry parameters. Any suitable oximeter, however, may be used.

FIG. 2 is a block diagram of an exemplary signal processing system 200of a pulse oximeter. Embodiments for carrying out the present techniquesmay be implemented as a part of a signal processing system, such assignal processing system 200, that processes optical signals for thepurposes of operating a pulse oximeter. For example, signal processingsystem 200 may be implemented as a software process that is executed bya processor of a pulse oximeter, such as the processor 122 of theoximeter 120 discussed above.

Block 202 represents the operations of a Signal Conditioning subsystem.Block 202 may receive digitized Red and IR signals, and may outputpre-processed Red and IR signals. The Signal Conditioning subsystemconditions signals to emphasize higher frequencies associated with thehuman plethysmograph and to attenuate lower frequencies associated withinterference from noise sources. The derivative-filtered plethysmographscharacteristically have a negative skewness. The signals may beconditioned by taking a first derivative to reduce or eliminate abaseline shift, and then low pass filtering with coefficients based onhardware characteristics. The Signal Conditioning subsystem may alsodivide the lowpass filtered values by the average of the respective IRor Red signals.

Block 204 represents the operations of a Pulse Identification andQualification subsystem. Block 204 may receive pre-processed Red and IRsignals, average pulse period, lowpass waveforms from the SignalConditioning subsystem, and/or ensemble averaging filter weights. Block204 may output pulse quality of individual pulses, degree of arrhythmia,pulse amplitude variations, and/or qualified pulse periods and age.

The Pulse Identification and Qualification subsystem identifies pulsesand qualifies the pulses as likely arterial pulses. Pulses may beidentified and qualified by applying a pre-trained neural net to the IRsignals and/or Red signals. Signal metrics describing pulses may becompared with pulse qualification criteria in order to qualify thepulses. A signal metric represents a feature of a signal, and it may beused to classify a signal. For example, a signal metric may be used toqualify a signal. Example signal metrics may describe features ofindividual pulses, such as amplitude, period, shape, ratio-of-ratios,and/or rise time. Example signal metrics may describe features of asequence of pulses such as the variability of the features of individualpulses (e.g., period variability).

A pulse qualification criterion may include parameters with which thesignal quality metrics may be compared. The parameters may be adjustedin response to variables such as filter parameters or signal metrics. Afilter parameter may refer to a parameter of a filter that may bemodified to change the filtering. An example embodiment of a method thatadjusts parameters in response to filter parameters is described withreference to FIGS. 3 and 4. An example embodiment of a method thatadjusts parameters in response to signal quality metrics is describedwith reference to FIG. 5.

Block 206 represents operations that compute signal quality metrics.Block 206 may receive: raw digitized Red and IR signals; degree ofarrhythmia, individual pulse quality, pulse amplitude variation;pre-processed Red and IR signals; and/or average pulse period. Block 206may output lowpass and ensemble averaging filter weights, normalizedpre-processed waveforms, and/or percent modulation. The signal qualitymetrics may be used to set parameters for other processes.

Block 208 represents operations that compute the average pulse periodfrom the received pulses. Block 208 may receive qualified pulse periodsand age, and it may output the average pulse period.

Block 210 represents the operations of a Lowpass Filter and EnsembleAveraging subsystem. Block 210 may receive normalized pre-processed Redand IR signals, average pulse period, and/or low pass filter weights andensemble averaging filter weights. Block 210 may output filtered Red andIR signals and/or age. The Lowpass Filter and Ensemble Averagingsubsystem filters and ensemble averages normalized and preprocessedsignals processed by block 206. Ensemble averaging may involveattenuating frequencies that are not of interest. For example, ensembleaveraging may involve attenuating frequencies that are not at theestimated pulse rate or harmonic. The Lowpass Filter and EnsembleAveraging subsystem may also track the age of the signal and/orfiltering results.

Block 212 represents operations that estimate the ratio-of-ratiosvariance for the filtered waveforms. Block 212 may receive filtered Redand IR signals, age, calibration coefficients, and/or response mode.Block 212 may output a ratio-of-ratios variance. A ratio-of-ratios isthe ratio of the absorbances of the red and infrared signals. Aratio-of-ratios variability metric indicates the variation of aratio-of-ratios. According to one embodiment, a ratio-of-ratios variancemay be adjusted according to a signal metric and a ratio-of-ratiosvariability metric. An example method is described with reference toFIG. 6. Block 216 represents operations that calculate oxygensaturation. Block 216 may receive ratio-of-ratios variability metricsand/or calibration coefficients, and may output oxygen saturationvalues.

Block 218 represents the operations of a Low Pass Filter and EnsembleAveraging subsystem. Block 218 may operate in a substantially similarmanner as block 210. Block 220 represents the operations of a FilteredPulse Identification and Qualification subsystem. Block 220 may operatein a substantially similar manner as block 204. The Filtered PulseIdentification and Qualification subsystem calculates and qualifies thepulse periods from the filtered waveforms. The results from thesubsystem may be used by block 222 if a pulse period is disqualified byblock 204.

Block 222 represents the operations of an Average Pulse Periods andCalculate Pulse Rate subsystem. Block 222 may receive qualified pulseperiods and age, and may output an average pulse period and/or a pulserate.

Block 224 represents the operations that detect venous pulsation. Block224 may receive the pre-processed Red and IR signals and age from block202, and it may output pulse rate and an indication of venous pulsation.Block 226 represents the operations that detect sensor-off and loss ofpulse amplitude.

Modifications, additions, or omissions may be made to system 200 withoutdeparting from the scope of the invention. The components of system 200may be integrated or separated according to particular needs. Moreover,the operations of system 200 may be performed by more, fewer, or othermodules. Additionally, operations of system 200 may be performed usingany suitable logic comprising software, hardware, firmware, or anysuitable combination of the preceding.

FIG. 3 is a flowchart illustrating one embodiment of a method foradjusting a noise gate parameter of a noise gate criterion in accordancewith an ensemble averaging weight. Ensemble averaging filtering mayreduce the noise level, which may allow for a lowered noise gate.Accordingly, the ensemble averaging weight, which indicates the degreeof ensemble averaging, may be used to adjust the noise gate parameter.

The method starts at step 300, where an ensemble averaging weight isestablished. An ensemble averaging weight may refer to a weight valuethat is used to calculate a weighted average of new samples andpreviously ensemble averaged samples from a previous pulse period. Anysuitable ensemble averaging weight may be used. According to oneembodiment, Ensemble_Averaging_Weight used by the Ensemble Averagingsubsystem may be used. If the pulses are not ensemble averaged, theweight may be set to default value, for example,Ensemble_Averaging_Weight=1.0.

Pulses are received at step 302. Variables describing the pulses areupdated at step 304. The variables may be updated in any suitablemanner. For example, the variables may be updated for each sample, priorto the every-potential-pulse, according to the following operations:

1. Baseline represents the average of input samples, and may be updatedaccording to the following equation:

Baseline_(t)=Baseline_(t−1) +c ₁ Δt*(Curr_Sample−Baseline_(t−1))

where t represents a sample index, c₁ represents a constant, Δtrepresents the sample interval given in seconds, Curr_Sample representsthe current sample. Constant c₁ may be any suitable value, such asc₁=0.01 for a one-second response time, given sampling interval Δt=10milliseconds.

2. Mean_Square represents the mean-square of input samples, and may beupdated according to the following equation:

Mean_Square_(t)=Mean_Square_(t−1)+k*((Curr_Sample−Baseline_(t))²−Mean_Square_(t−1))

where k represents a constant selected to yield a Mean_Square with aparticular response time.

As an example, k may be determined according to the following equationto yield a Mean_Square with a response time of one second or one pulse,whichever is shorter:

${k = {\max \left( {\frac{1}{Avg\_ Period},{\Delta \; t}} \right)}},{{{{if}\mspace{14mu} {Avg\_ Period}} > 0};}$${\max \left( {\frac{1}{{Potential\_ Pulse}{\_ Period}},{\Delta \; t}} \right)},{{{if}\mspace{14mu} {Potential\_ Pulse}{\_ Period}} > 0},{{{AND}\mspace{14mu} {previous}\mspace{14mu} {Potential\_ Pulse}{\_ Period}} = 0},{{{{AND}\mspace{14mu} {Avg\_ Period}} = 0};{{and}\mspace{14mu} \Delta \; t}},{{{if}\mspace{14mu} {Avg\_ Period}} = 0.}$

where Avg_Period represents the average pulse period, andPotential_Pulse_Period represents the pulse period of a potential pulse.A short response time may allow Mean_Square to decline quickly when amotion artifact ends, which may minimize the likelihood of ignoring realpulses. During the first potential pulse, the above equation for k maygive equal weight to each sample for up to one second, which may preventinitial underestimates of Mean_Square_(t) as it diverges from zero.

3. Gated_RMS represents the square root of Mean_Square, and may beupdated according to the following equation:

${Gated\_ RMS}_{t} = \left\{ \begin{matrix}\sqrt{{Mean\_ Square}_{t}} & {{{if}\mspace{14mu} {Curr\_ Sample}} > {Baseline}_{t}} \\{Gated\_ RMS}_{t - I} & {{{if}\mspace{14mu} {Curr\_ Sample}} \leq {Baseline}_{t}}\end{matrix} \right.$

At step 308, the pulse period may exceed a threshold that indicates thata pulse has just been missed or will occur shortly. The threshold mayhave any suitable value, such as two seconds. If the pulse periodexceeds the threshold at step 308, the method proceeds to step 312,where mean square parameters are reduced. According to one embodiment,the mean square parameters may include Mean_Square and Gated_RMS, andmay be reduced according to the following equations:

Gated_RMS_(t)=(1−Δt)Gated_RMS_(t)

Mean_Square_(t)=(1−Δt)Mean_Square_(t)

The method then proceeds to step 316. If the pulse period does notexceed the threshold at step 308, the method proceeds directly to step316.

The minimum phase of a potential pulse is identified at step 316. Theminimum phase may be identified when the value of Curr_Sample_(t) has aspecified relationship to previous or subsequent Curr_Sample values. Forexample, a minimum phase may be identified when Curr_Sample_(t) exceedssamples that occurred in the previous 100 to 150 milliseconds. Steps 320through 332 describe updating the noise gate parameter of a noise gatecriterion. A noise gate criterion may refer to a pulse qualificationcriterion that qualifies a pulse according to a noise gate. A noise gateparameter may refer to a parameter that controls the threshold thatidentifies whether a waveform includes noise. For example, the noisegate parameter may be adjusted to reduce the noise gate level if theensemble averaging weight indicates an increased degree of ensembleaveraging.

According to one embodiment, the noise gate parameter Noise_Gate may bedefined according to the following equation:

Noise_Gate=max(n*Gated_RMS,(c₂+c₃*E_A_W)*m*Noise_Floor/IR_DC)  4

where n represents the noise gate multiplier, E_A_W representsEnsemble_Averaging_Weight, m represents a noise floor multiplier, IR_DCrepresents the current infrared (IR) A/D values, Noise_Floor representsa minimum noise level associated with the oximetry hardware, whichdetermines a minimum value for the noise gate, and c₂ and c₃ representany suitable constants, for example, c₂=c₃=0.5.

The noise floor multiplier of the noise gate parameter is set at step320. The noise floor multiplier may be set in any suitable manner. As anexample, noise floor multiplier m may initially be set to an unit value,for example, m=1.0. Subsequent values of noise floor multiplier m may beset according to the following equation:

${m = {1 - \frac{c_{4} \star \left( {{c_{5} \star {Avg\_ Period}} - {{{Avg\_ Period} - {Pulse\_ Period}}}} \right)}{c_{5} \star {Avg\_ Period}}}},{{{{if}\mspace{14mu} {{{Avg\_ Period} - {Pulse\_ Period}}}} < {c_{5} \star {Avg\_ period}}};}$${1 - \frac{c_{4} \star \left( {{c_{5} \star {Avg\_ Period}} - {\begin{matrix}{{Avg\_ Period} -} \\{{Potential\_ Pulse}{\_ Period}}\end{matrix}}} \right)}{c_{5} \star {Avg\_ Period}}},{{{{if}\mspace{14mu} {{{Avg\_ Period} - {{Potential\_ Pulse}{\_ Period}}}}} < {c_{5} \star {Avg\_ Period}}};}$and${1 + \frac{c_{4} \star l}{c_{5} \star {Avg\_ Period}}},{{{if}\mspace{14mu} {Potential\_ Pulse}{\_ Period}} < {c_{6} \star {{Avg\_ Period}.}}}$

where Pulse_Period represents the pulse period of the current pulse, lrepresents a parameter, and c₄, c₅, and c₆ represent constants havingany suitable values, for example, c₄=0.5, c₅=0.25, and c₆=0.75.

Parameter l may be given by the following equation:

l=w((1−c ₅)*Avg_Period−Potential_Pulse_Period)+(1−w)*(c₅*Avg_Period−min(|c ₄*Avg_Period−Potential_Pulse_Period|,c₅*Avg_Period))

where:

w=(Frequency_Ratio−c₇)/c₈

w=max(0, min(1, w))

and where Frequency_Ratio represents the frequency content ofplethysmograph relative to the pulse rate, and c₇ and c₈ representconstants having any suitable values, for example, c₇=1.25 and c₈=0.60.

According to one embodiment, if Avg_Period is zero, then noise floormultiplier m may be calculated using the previous Potential_Pulse_Periodin place of Avg_Period, provided that the previousPotential_Pulse_Period is acceptable for modifying noise floormultiplier m. For example, the duration of the previousPotential_Pulse_Period may be required to satisfy a threshold indicatingthat the pulse periods are less likely to reflect noise. According tothe example, if Avg_Period is zero, then Mean_Square is establishedaccording to the following equation:

Mean_Square_(t)=max(Mean_Square_(t) ,c ₉*Potential_Pulse_Amp²)

where Potential_Pulse_Amp represents the amplitude of a pulse, and c₉represents a constant having any suitable value, for example, c₉=0.12.Potential_Pulse_Amp may be given asPotential_Pulse_Amp=Potential_Pulse_Max−Potential_Pulse_Min, wherePotential_Pulse_Max represents the maximum of the previous severalsamples at the end of the maximum phase preceding the minimum phase, andPotential_Pulse_Min represents the minimum of the previous severalsamples at the end of the minimum phase.

The noise gate multiplier n is established at step 328. Noise gatemultiplier n may be established in any suitable manner. According to oneembodiment, noise gate multiplier n may be established according to thefollowing equation:

n=c ₁₀ *m*bound(c ₁₁+Potential_Pulse_Skew,c ₁₂ ,c ₁₃), ifPulse_Period>Potential_Pulse_Period;

c ₁₀ *m*bound(c ₁₄+Potential_Pulse_Skew,c ₁₂ ,c ₁₃), if Skew_DerivativeInput Weight>c ₁₅; and

c₁₀*m otherwise.

where Skew_Derivative_Input_Weight represents a weight used to combinethe Curr_Sample waveform with its derivative to obtain a waveform havinga more negative skew, Potential_Pulse_Skew represents the skewness ofthe samples in this combined waveform over the duration of thePotential_Pulse_Period, and c₁₀, c₁₁, c₁₂, c₁₃, c₁₄, and c₁₅ representconstants having any suitable values, for example, c₁₀=0.85, c₁₁=1.5,c₁₂=0.4, c₁₃=1.0, c₁₄=2.0, and c₁₅=8.0. The notation bound(a, b, c) isused to denote min(max(a, b), c).

The noise gate parameter is adjusted according to the noise floormultiplier and the noise gate multiplier at step 332. After updating thenoise gate parameter, the method ends.

Modifications, additions, or omissions may be made to the method withoutdeparting from the scope of the invention. The method may include more,fewer, or other steps. Additionally, steps may be performed in anysuitable order without departing from the scope of the invention.

FIG. 4 is a flowchart illustrating one embodiment of a method foradjusting a pulse period criterion in accordance with an ensembleaveraging weight. According to the embodiment, an ensemble averagingweight may indicate that a qualified pulse is not likely to have a pulseperiod that is substantially shorter than an average pulse period.Accordingly, a pulse period criterion may be adjusted to disqualify apulse having a pulse period that is substantially shorter than anaverage pulse period.

The method starts at step 350, where an ensemble averaging weight isestablished. Pulses are received at step 354. Steps 358 through 370describe evaluating the received pulses in accordance with pulsequalification criteria. The pulse qualification criteria may be appliedin any suitable order. According to one embodiment, the pulsequalification criteria are applied in the order of the steps, and if apulse fails one criterion, subsequent criteria are not applied.

Pulse qualification criteria comprising one or more sample criteria areapplied at step 358. Any suitable sample criteria SC_(i) may be used,for example:

SC₁: Pulse_Min<Pulse_Avg<Pulse_Max

SC₂: Avg_Min<Pulse_Avg<Pulse_Max

where Pulse_Min represents the minimum of the previous several samplesat the end of the minimum phase, Pulse_Avg represents the average of thesamples of a period, and Pulse_Max represents the maximum of theprevious several samples at the end of the maximum phase. Pulse_Avg maybe expressed as

${{Pulse\_ Avg} = \frac{Pulse\_ Sum}{Pulse\_ Period}},$

where Pulse_Sum represents the sum of the samples of a pulse, andPulse_Period represents the pulse period of the current pulse. Pulse_Summay be updated at every sample and reset to zero after calculatingPulse_Avg.

Pulse qualification criteria comprising one or more output criteria areapplied at step 362. Any suitable output criteria OC_(i) may be used,for example:

OC₁: Pulse_Qual_NN_Output_(t)>NN_Thresh

OC₂: Pulse_Qual_NN_Output_Integral_(t)≧c₈, if no pulses have beenqualified yet

where Pulse_Qual_NN_Output represents the output of the pre-trainedpulse qualification neural net, NN_Thresh represents a neural netthreshold that may be used to qualify a pulse,Pulse_Qual_Output_Integral represents the integral ofPulse_Qual_NN_Output_(t), and c₈ represents a constant. The output ofthe neural net may range from 0 to 1.

In one example, NN_Thresh may be given by the following equation:

NN_Thresh=c ₁ +c ₂*(Period_Ratio−c ₃); and

c₄, if there are no qualified pulses, for example, if Avg_Period=0 wherePeriod_Ratio represents the ratio of the current period and the averageperiod, and c₁, c₂, c₃, and c₄ represent constants having any suitablevalues, for example, c₁=0.4, c₂=0.25, c₃=1.0, and c₄=0.5. Period_Ratiomay be given as

${Period\_ Ratio} = {{\min \left( {2,{\max \left( {\frac{Pulse\_ Period}{Avg\_ Period},\frac{Avg\_ Period}{Pulse\_ Period}} \right)}} \right)}.}$

In one example, Pulse_Qual_Output_Integral may be given by the followingequation:

Pulse_Qual_Output_Integral_(t)=min(Pulse_Qual_Output_Integral_(t−1)+Pulse_Qual_(—)NN_Output_(t) −c ₅ , c ₆); and

0, if Pulse_Qual_NN_Output_(t)≦c₇

where c₅, c₆, and c₇ represent constants having any suitable values, forexample, c₅=0.5, c₆=100, and c₇=0.5, and the subscript t⁻¹ denotes theprevious pulse. In one example, constant c₈ may be set such that theinitial estimate for Avg_Period is determined from either a very goodpulse (for example, >95% probability of being an arterial pulse) ormultiple acceptable successive pulses.

Pulse qualification criteria comprising one or more initializationcriteria are applied at step 366. Any suitable initialization criterionIC_(i) may be used, for example:

IC₁: |Pulse_Period−Last_Pulse_Period|≦c₉*Initial_Pulse_Count*Pulse_Period

where Pulse_Period represents the pulse period of the current pulse,Last_Pulse_Period represents the Pulse_Period for the previousnon-ignored pulse, Initial_Pulse_Count represents the number ofnon-ignored pulses found since re-initialization, and c₉ represents aconstant having any suitable value, for example, c₉=0.1.Initial_Pulse_Count may be incremented after a pulse is evaluatedaccording to the pulse qualification criteria. Criterion IC₁ requiresthat consecutive pulse periods agree closely, but gradually relaxes itsthreshold with each successive pulse, which may reduce or preventinaccurate initial pulse estimates. According to one embodiment, thefirst pulse may be rejected by requiring Initial_Pulse_Count>1.

IC₂: Path_Ratio<c₁₀, if no pulses have been qualified yet wherePath_Ratio represents the ratio of the path length and the pulseamplitude, and c₁₀ represents a constant having any suitable value.Path_Ratio may be given by

$\frac{Path\_ Length}{{Pulse\_ Max} - {{Pulse}\mspace{14mu} {Min}}},$

where Path_Length represents the sum of sample-to-sample differences forthe samples of a pulse. Constant c₁₀ may be selected to reduce thelikelihood of initializing the pulse-rate estimate to values that aretoo low under noisy conditions. For example, c₁₀=4.0.

A pulse period criterion is adjusted according to the ensemble averagingweight at step 368. A pulse period criterion may refer to a pulsequalification criterion that qualifies a pulse according to a pulseperiod. Any suitable pulse period criterion PPC_(i) may be used.According to one embodiment, a pulse with a shorter-than-average periodmay be disqualified if the ensemble averaging weight indicates thatqualified pulses are less likely to have shorter-than-average period. Asan example, pulse period criterion PPC₁ may be used and adjustedaccording to the following:

PPC ₁:(Avg_Period−Pulse_Period)≦2*Avg_Period*min(Ensemble_Averaging_Weight_(t),Ensemble_Averaging_Weight_(t−1)),if Avg_Period>0

where Avg_Period represents an estimate of the average pulse period,Pulse_Period represents the pulse period of the current pulse. Pulsequalification criteria comprising the pulse period criterion are appliedat step 370.

The pulses are qualified or disqualified according to the evaluations atstep 374. The pulses may be qualified or disqualified according to theevaluations in any suitable manner. According to one embodiment, a pulsethat satisfies the above pulse qualification criteria is designated asqualified, and a pulse that fails to satisfy any of the above pulsequalification criteria is designated as non-qualified. After qualifyingthe pulses, the method ends.

Modifications, additions, or omissions may be made to the method withoutdeparting from the scope of the invention. The method may include more,fewer, or other steps. Additionally, steps may be performed in anysuitable order without departing from the scope of the invention.

FIG. 5 is a flowchart illustrating one embodiment of a method foradjusting a pulse amplitude criterion in accordance with an averagepulse amplitude and an average pulse period. According to oneembodiment, a pulse amplitude criterion may disqualify a pulse with anamplitude that is much smaller than (e.g., more than three timessmaller) an average amplitude and a period that is much greater that anaverage period. The pulse amplitude criterion may reduce the likelihoodof qualifying instrument noise when the pulse amplitude suddenlydisappears.

The method starts at step 400, where pulses are received. Steps 412through 424 describe evaluating the received pulses in accordance withpulse qualification criteria. The pulse qualification criteria may beapplied in any suitable order. According to one embodiment, the pulsequalification criteria are applied in the order of the steps, and if apulse fails one criterion, subsequent criteria are not applied.

Pulse qualification criteria comprising one or more sample criteria areapplied at step 412. Any suitable sample criterion SC_(i) may be used,for example, SC₁ and SC₂. Pulse qualification criteria comprising one ormore output criteria are applied at step 416. Any suitable outputcriterion OC_(i) may be used, for example, OC₁ and OC₂. Pulsequalification criteria comprising one or more initialization criteriaare applied at step 420. Any suitable initialization criterion IC_(i)may be used, for example, IC₁ and IC₂.

Pulse qualification criteria comprising one or more pulse amplitudecriteria are applied at step 424. Any suitable pulse amplitude criterionPSC_(i) may be used. According to one embodiment, a pulse amplitudecriterion may disqualify a pulse with an amplitude that is much smallerthan (e.g., more than three times smaller) an average amplitude and aperiod that is much greater that an average period. The pulse amplitudecriterion may reduce the likelihood of qualifying instrument noise whenthe pulse amplitude suddenly disappears.

An example pulse amplitude criterion PSC₁ may be expressed as:

PSC₁: Sum_Amp_Diff≧−c₁; OR

Period_Ratio≦c₂; OR

Period_Ratio≦(c₁+Sum_Amp_Diff/c₃), provided thatPulse_Period>Avg_Period, pulses have been previously qualified, and asignificant number of pulses have been previously evaluated;

where Sum_Amp_Diff represents the sum of amplitudes, Period_Ratiorepresents the ratio of the current period and the average period, andc₁, c₂, and c₃ represent constants having any suitable values, forexample, c₁=2.3, c₂=1.3, and c₃=6.

According to one embodiment, Sum_Amp_Diff may be defined according tothe following equation:

Sum_Amp_Diff=Amp_Diff2_(t)+Amp_Diff2_(t−1)

where:

Amp_Diff2=ln(Pulse_Amp_(t))/Avg_Ln_Pulse_Amp_(t)

where Avg_Ln_Pulse_Amp represents a variable-weight filtered version ofln(Pulse_Amp), with a low initial value, and Pulse_Amp represents theamplitude of a pulse in the Curr_Sample waveform.

The pulses are qualified or disqualified according to the evaluations atstep 428. The pulses may be qualified or disqualified according to theevaluations in any suitable manner. According to one embodiment, a pulsethat satisfies the above pulse qualification criteria is designated asqualified, and a pulse that fails to satisfy any of the above pulsequalification criteria is designated as non-qualified. After the pulsesare qualified, the method ends.

Modifications, additions, or omissions may be made to the method withoutdeparting from the scope of the invention. The method may include more,fewer, or other steps. Additionally, steps may be performed in anysuitable order without departing from the scope of the invention.

FIG. 6 is a flowchart illustrating one embodiment of a method foradjusting a saturation weight in accordance with a pulse quality metric.A pulse quality metric is a signal metric indicating the quality of oneor more pulses. For example, a pulse quality metric may describe acharacteristic such as pulse shape or beat-to-beat variability. Asaturation weight may refer to a weight used for filtering saturationestimates. The metric may be used to adjust a saturation weight, whichmay enhance accuracy under conditions such as interference from a noisesource, low perfusion, or electronic interference.

The method starts at step 450, where a saturation weight is established.The method may be applied to any suitable filter that filters saturationestimates. According to one embodiment, the filter may comprise a Kalmanfilter. For a Kalman filter, the saturation s may be updated for eachsample from an estimation error P and Kalman gain K according to thefollowing equation:

s _(t) =s _(t−1) +K _(t)(v _(t) −u _(t) s _(t−1))

where:

K _(t) =P _(t) u _(t) R _(t−1) ⁻¹

P _(t)=((P _(t−1) ++Q)⁻¹ +u _(t) ² R _(t−1) ⁻¹)⁻¹

and u and v represent inputs, Q represents the variance of saturation s,and R represents the variance of noise. Inputs u and v may representinputs calculated from the IR and Red plethysmographs utilizingcoefficients defined by encoder 116. The ratio v/u may converge to theoxygen saturation estimate in the absence of artifacts.

According to one embodiment, noise variance R may be given by thefollowing equation:

$R = \left( {\sum\limits_{i = 0}^{N - 1}\frac{\left( {v_{t - i} - {u_{t - i}s_{t - i}}} \right)^{2}}{N}} \right)$

where N represents a suitable number of samples, such as one pulseperiod or one second.

Saturation weight w_(t) may be given by the following equation:

$w_{t} = {K_{t}{\sum\limits_{i = 0}^{N - 1}\frac{u_{t}^{2}}{N}}}$

Saturation weight w_(t) depends on Kalman gain K, which in turn dependson noise variance R. Accordingly, a change in the noise variance mayyield a change in the saturation weight. Noise variance R may bedependent, at least in part, on the filtered saturation output of theKalman filter.

A pulse quality metric is determined at step 462. The pulse qualitymetric Pulse_Qual may reflect the quality of the most recently evaluatedpulse. According to one embodiment, the pulse quality metrics range from0 to 1.

The saturation filtering may be in one of multiple response modes basedon the response time of the filtering at step 466. As an example, theresponse modes may include a fast response mode, normal response mode,or slow response mode, where the fast response mode has a time of lessthan 4 seconds, normal response mode has a time of greater than 4 andless than 6, or slow response mode has a time of more than 6 seconds.The response mode may be user-selected or automatically selected.

If the saturation filtering is in a fast or a normal response mode atstep 466, the method proceeds to step 468, where the noise variance isadjusted. According to one embodiment, the noise variance in the fastresponse mode may be adjusted according to the following equation:

$R_{t}^{- 1} = \left( {\frac{b}{n}{\sum\limits_{i = 0}^{N - 1}u_{t - i}^{2}}} \right)^{- 1}$

where b is a constant selected in accordance with the response mode.According to the embodiment, the noise variance in the normal responsemode may be adjusted according to the following equation:

$R_{t}^{- 1} = {\max \left( {{\min \left( {R_{t - 1},\rho_{t - 1}} \right)},{\frac{b}{n}{\sum\limits_{i = 0}^{N - 1}u_{t - i}^{2}}}} \right)}^{- 1}$

where ρ represents a second noise variance estimate. The secondnoise-variance estimate is designed to be less sensitive to rapidphysiological saturation changes.

If the saturation filtering is in the slow response mode at step 466,the method proceeds to step 470, where the noise variance is adjusted inaccordance with pulse quality. According to one embodiment, the noisevariance in the fast response mode may be adjusted according to thefollowing equation:

$R_{t}^{- 1} = {\max \left( {\frac{\min \left( {R_{t - 1},\rho_{t - 1}} \right)}{{temp}^{2}},{\frac{b}{n}{\sum\limits_{i = 0}^{N - 1}u_{t - i}^{2}}}} \right)}^{- 1}$

where:

temp=max(min(c ₁*Pulse_Qual,c ₂),c ₃)

and c₁, c₂, and c₃ represent any suitable constants, for example, c₁=2,c₂=1, and c₃=0.01. According to the above equation, a change in pulsequality may yield a change in noise variance. As discussed above, achange in the noise variance may yield a change in the saturationweight. Accordingly, saturation weight may be adjusted in accordancewith the pulse quality.

The saturation weight is adjusted in accordance with the noise variancec at step 474. After adjusting the saturation weight, the method ends.

Modifications, additions, or omissions may be made to the method withoutdeparting from the scope of the invention. The method may include more,fewer, or other steps. Additionally, steps may be performed in anysuitable order without departing from the scope of the invention.

FIG. 7 is a flowchart illustrating another embodiment of a method foradjusting a saturation weight in accordance with a ratio-of-ratiosvariability metric and a pulse quality metric. A ratio-of-ratiosvariability metric indicates the variation of a ratio-of-ratios, where aratio-of-ratios is the ratio of the absorbances of red and infraredsignals. A pulse quality metric is a signal metric indicating thequality of one or more pulses. For example, a pulse quality metric maydescribe a characteristic such as pulse shape or beat-to-beatvariability. A saturation weight may refer to a weight used forfiltering saturation estimates. The metrics may be used to adjust asaturation weight, which may enhance accuracy under conditions such asinterference from a noise source, low perfusion, or electronicinterference.

The method starts at step 550, where a saturation weight is established.A ratio-of-ratios variability metric is determined at step 558. Anysuitable ratio-of-ratios variability metric may be used. According toone embodiment, the ratio-of-ratios RoR may be determined from infraredand red input samples collected over a time period of any suitableduration, for example, less than 5 seconds, such as 3 seconds. RoR maybe given by the following equation:

$\overset{\_}{RoR} = \frac{\sum\limits_{t = 0}^{N - 1}\left( {{IR}_{t} \star {Red}_{t}} \right)}{\sum\limits_{t = 0}^{N - 1}{IR}_{t}^{2}}$

where Red represents red input values, IR represents infrared inputvalues, and N represents the time period divided by the sample time.

According to the embodiment, a ratio-of-ratios variability metricRoR_Variance_Per may be given by the following equation:

${{RoR\_ Variance}{\_ Per}} = \sqrt{\frac{SumDiffSq}{\sum\limits_{t = 0}^{N - 1}{Red}_{t}^{2}}}$where:${SumDiffSq} = {\sum\limits_{t = 0}^{N - 1}\left( {{{IR}_{t} \star \overset{\_}{RoR}} - {Red}_{t}} \right)^{2}}$

A pulse quality metric is determined at step 562. The pulse qualitymetric Pulse_Qual may reflect the quality of the most recently evaluatedpulse.

The saturation filtering may be in one of multiple response modes basedon the response time of the filtering at step 566. As an example, theresponse modes may include a fast response mode, normal response mode,or slow response mode. If the saturation filtering is in a fast or anormal response mode at step 566, the method proceeds to step 568, wherethe saturation weight is calculated from the ratio-of-ratios variancemetric. According to one embodiment, the saturation weight may beadjusted by first calculating an intermediate value Sat_Noise_Peraccording to the following equation:

Sat_Noise_Per = RoR_Noise_Per ⋆ dSat_dRoR where:RoR_Noise_Per = RoR_Variance_Per ⋆ RoR where${RoR} = \frac{\sum\limits_{t = 0}^{N - 1}R_{t}^{2}}{\sum\limits_{t = 0}^{N - 1}{IR}_{t}^{2}}$

and where dSat_dRoR represents the derivative of the saturation withrespect to the ratio-of-ratios at a ratio-of-ratios value equal to RoR.This derivative may be calculated using the calibration coefficientsselected by the oximeter based on the signals from the encoder 116.According to one embodiment, if Sat_Noise_Per is greater then a maximumthreshold, Sat_Noise_Per may be set to a maximum value.

The saturation weight may be adjusted using the intermediate valueSat_Noise_Per. If Sat_Noise_Per is greater than a threshold c₄, such asc₄=0.09, then saturation weight may be adjusted according to thefollowing equation:

w _(t) =w _(t) *c ₄/Sat_Noise_Per

According to the embodiment, before the first second of filtering haselapsed, the minimum filter weight w_(m) may be set at w_(m)=1.0.Otherwise, minimum filter weight w_(m) may be set at w_(m)=0.5 dividedby the number of seconds.

If the saturation filtering is in the slow response mode at step 566,the method proceeds to step 570, where the saturation weight iscalculated from the ratio-of-ratios variance metric and then adjusted inaccordance with the pulse quality metric. The saturation weight w_(t)may be calculated in a manner similar to that of step 568, except thatw_(t) may be adjusted according to the pulse quality metric. Forexample, the equation for RoR_Noise_Per may be modified to:

RoR_Noise_Per RoR_Variance_Per*RoR/temp

where temp=max(min(c₁*Pulse_Qual, c₂), c₃), and c₁, c₂, and C₃ representany suitable constants, for example, c₁=2, c₂=1, and c₃=0.1.

Saturation is calculated at step 574. Saturation may be calculatedaccording to the following equation:

Saturation_(t) =w _(t)*Raw_Saturation_(t)+(1−w _(t))*Saturation_(t−1)

where Raw_Saturation_(t) is calculated from RoR using the calibrationcoefficients selected by the oximeter based on the signals from theencoder 116. After calculating the saturation, the method ends.

Modifications, additions, or omissions may be made to the method withoutdeparting from the scope of the invention. The method may include more,fewer, or other steps. Additionally, steps may be performed in anysuitable order without departing from the scope of the invention.

As an example, the above-described saturation weight might be used tofilter the ratio-of-ratios prior to saturation calculation, rather thancalculating saturation first and then filtering the ratio-of-ratios.According to one embodiment, a ratio-of-ratios weight may be establishedin accordance with the ratio-of-ratios variability metric and the pulsequality metric. The ratio-of-ratios weight may be established in amanner similar to that of step 570. The ratio-of-ratios may be filteredin accordance with the ratio-of-ratios weight, and the saturationcalculated according to the filtered ratio-of ratios.

Certain embodiments of the invention may provide one or more technicaladvantages. A technical advantage of one embodiment may be that a noisegate parameter of a noise gate criterion may be adjusted in accordancewith an ensemble averaging weight. Ensemble averaging filtering mayreduce the noise level, which may allow for a lowered noise gate.Accordingly, the ensemble averaging weight, which indicates the degreeof ensemble averaging, may be used to adjust the noise gate parameter.

Another technical advantage of one embodiment may be that a pulse periodcriterion may be adjusted in accordance with an ensemble averagingweight. According to the embodiment, an ensemble averaging weight mayindicate that a qualified pulse is not likely to have a pulse periodthat is substantially shorter than an average pulse period. Accordingly,a pulse period criterion may be adjusted to disqualify a pulse having apulse period that is substantially shorter than an average pulse period.

Another technical advantage of one embodiment may be that a pulseamplitude criterion may be adjusted in accordance with an average pulseamplitude and an average pulse period. According to one embodiment, apulse amplitude criterion may disqualify a pulse with an amplitude thatis much smaller than (e.g., more than three times smaller) an averageamplitude and a period that is much greater that an average period. Thepulse amplitude criterion may reduce the likelihood of qualifyinginstrument noise when the pulse amplitude suddenly disappears.

Another technical advantage of one embodiment may be that a saturationweight may be adjusted in accordance with a ratio-of-ratios variabilitymetric and a pulse quality metric. The metrics may be used to adjust asaturation weight, which may enhance accuracy under conditions such asmotion artifact, low perfusion, or electronic interference.

While the invention may be susceptible to various modifications andalternative forms, specific embodiments have been shown by way ofexample in the drawings and have been described in detail herein.However, it should be understood that the invention is not intended tobe limited to the particular forms disclosed. Rather, the invention isto cover all modifications, equivalents, and alternatives falling withinthe spirit and scope of the invention as defined by the followingappended claims.

1. A method for adjusting a weight of a saturation filtering process,comprising: receiving a signal from a sensor representing one or morepulses, the signal generated in response to detecting light scatteredfrom blood perfused tissue, the light comprising a first wavelength anda second wavelength; and using a processor: determining aratio-of-ratios variability metric indicating the variation of aratio-of-ratios, a ratio-of-ratios representing the ratio of absorbancesof the first wavelength and the second wavelength; determining a pulsequality metric indicating the quality of the one or more pulses; andadjusting a saturation weight in accordance with the ratio-of-ratiosvariability metric and the pulse quality metric, the saturation weightrepresenting a weight used for a filtering process operable to filter asaturation estimate of the blood perfused tissue.
 2. The method of claim1, comprising: establishing that the filtering process is in apredetermined response mode.
 3. The method of claim 1, wherein adjustinga saturation weight in accordance with the ratio-of-ratios variabilitymetric and the pulse quality metric further comprises: modifying a noisevariance in accordance with the pulse quality metric; and adjusting thesaturation weight in accordance with the modified noise variance.
 4. Themethod of claim 1, wherein the saturation weight is calculated by aKalman filter.
 5. A method for calculating saturation, comprising:receiving a signal from a sensor representing one or more pulses, thesignal generated in response to detecting light scattered from bloodperfused tissue, the light comprising a first wavelength and a secondwavelength; using a processor: determining a pulse quality metricindicating the quality of the one or more pulses; determining aratio-of-ratios variability metric indicating the variation of aratio-of-ratios, a ratio-of-ratios representing the ratio of absorbancesof the first wavelength and the second wavelength; establishing aratio-of-ratios weight in accordance with the ratio-of-ratiosvariability metric and the pulse quality metric; filtering theratio-of-ratios in accordance with the ratio-of-ratios weight; andcalculating saturation according to the filtered ratio-of ratios; anddisplaying the saturation on a display.
 6. The method of claim 5,comprising: establishing that the filtering process is in apredetermined response mode.
 7. A system operable to adjust a weight ofa saturation filtering process, comprising: an interface operable to:receive a signal representing one or more pulses, the signal generatedin response to detecting light scattered from blood perfused tissue, thelight comprising a first wavelength and a second wavelength; and aprocessor operable to: determine a ratio-of-ratios variability metricindicating the variation of a ratio-of-ratios, a ratio-of-ratiosrepresenting the ratio of absorbances of the first wavelength and thesecond wavelength; determine a pulse quality metric indicating thequality of the one or more pulses; and adjust a saturation weight inaccordance with the ratio-of-ratios variability metric and the pulsequality metric, a saturation weight representing a weight used for afiltering process operable to filter the saturation estimate of theblood perfused tissue.
 8. The system of claim 7, the processor furtheroperable to: establish that the filtering process is in a predeterminedresponse mode.
 9. The system of claim 7, the processor further operableto adjust a saturation weight in accordance with the ratio-of-ratiosvariability metric and the pulse quality metric by: modifying a noisevariance in accordance with the pulse quality metric; and adjusting thesaturation weight in accordance with the modified noise variance. 10.The system of claim 7, wherein the saturation weight is calculated by aKalman filter.
 11. A system for adjusting a weight of a saturationfiltering process, comprising: an interface operable to: receive asignal representing one or more pulses, the signal generated in responseto detecting light scattered from blood perfused tissue, the lightcomprising a first wavelength and a second wavelength; and a processoroperable to: determine a pulse quality metric indicating the quality ofthe one or more pulses; determine a ratio-of-ratios variability metricindicating the variation of a ratio-of-ratios, a ratio-of-ratiosrepresenting the ratio of absorbances of the first wavelength and thesecond wavelength; establish a ratio-of-ratios weight in accordance withthe ratio-of-ratios variability metric and the pulse quality metric;filter the ratio-of-ratios in accordance with the ratio-of-ratiosweight; and calculate saturation according to the filtered ratio-ofratios.
 12. The system of claim 11, the processor further operable toestablish that the filtering process is in a predetermined responsemode.
 13. A system for adjusting a weight of a saturation filteringprocess, comprising: means for receiving a signal representing one ormore pulses, the signal generated in response to detecting lightscattered from blood perfused tissue, the light comprising a firstwavelength and a second wavelength; means for determining aratio-of-ratios variability metric indicating the variation of aratio-of-ratios, a ratio-of-ratios representing the ratio of absorbancesof the first wavelength and the second wavelength; means for determininga pulse quality metric indicating the quality of the one or more pulses;and means for adjusting a saturation weight in accordance with theratio-of-ratios variability metric and the pulse quality metric, thesaturation weight representing a weight used for a filtering processoperable to filter a saturation estimate of the blood perfused tissue.14. A system operable to adjust a weight of a saturation filteringprocess, comprising: an interface operable to: receive a signalrepresenting one or more pulses, the signal generated in response todetecting light scattered from blood perfused tissue, the lightcomprising a first wavelength and a second wavelength; and a processoroperable to: determine a ratio-of-ratios variability metric indicatingthe variation of a ratio-of-ratios, a ratio-of-ratios representing theratio of absorbances of the first wavelength and the second wavelength;determine a pulse quality metric indicating the quality of the one ormore pulses; establish that the filtering process is in a predeterminedresponse mode; and adjust a saturation weight in accordance with theratio-of-ratios variability metric and the pulse quality metric, thesaturation weight representing a weight used for a filtering processoperable to filter a saturation estimate of the blood perfused tissue,the saturation weight calculated by a Kalman filter, the saturationweight adjusted by: modifying a noise variance in accordance with thepulse quality metric; and adjusting the saturation weight in accordancewith the modified noise variance.
 15. A system, comprising: a sensorcomprising: one or more light sources adapted to emit light at a firstwavelength and a second wavelength into blood perfused tissue; and aphotodetector adapted to detect the light scattered from the bloodperfused tissue; and a monitor comprising: an interface adapted tocouple the sensor to the monitor, the interface operable to receive asignal representing one or more pulses based on the detected light; anda processor operable to: determine a ratio-of-ratios variability metricindicating the variation of a ratio-of-ratios, a ratio-of-ratiosrepresenting the ratio of absorbances of the first wavelength and thesecond wavelength; determine a pulse quality metric indicating thequality of the one or more pulses; and adjust a saturation weight inaccordance with the ratio-of-ratios variability metric and the pulsequality metric, a saturation weight representing a weight used for afiltering process operable to filter the saturation estimate of theblood perfused tissue.
 16. The system of claim 15, the processor furtheroperable to adjust a saturation weight in accordance with theratio-of-ratios variability metric and the pulse quality metric by:modifying a noise variance in accordance with the pulse quality metric;and adjusting the saturation weight in accordance with the modifiednoise variance.
 17. The system of claim 15, wherein the saturationweight is calculated by a Kalman filter.
 18. A system, comprising: asensor comprising: one or more light sources adapted to emit light at afirst wavelength and a second wavelength into blood perfused tissue; anda photodetector adapted to detect the light scattered from the bloodperfused tissue; and a monitor comprising: an interface adapted tocouple the sensor to the monitor, the interface operable to receive asignal representing one or more pulses based on the detected light; aprocessor operable to: determine a pulse quality metric indicating thequality of the one or more pulses; determine a ratio-of-ratiosvariability metric indicating the variation of a ratio-of-ratios, aratio-of-ratios representing the ratio of absorbances of the firstwavelength and the second wavelength; establish a ratio-of-ratios weightin accordance with the ratio-of-ratios variability metric and the pulsequality metric; filter the ratio-of-ratios in accordance with theratio-of-ratios weight; and calculate saturation according to thefiltered ratio-of ratios; and a display operable to display thecalculated saturation.
 19. The system of claim 18, the processor furtheroperable to: establish that the filtering process is in a predeterminedresponse mode.